31 results about "Service placement" patented technology

An approach efficiently and dynamically places services within a multi-node system when expanding or contracting services, that is, increasing and decreasing the number of instances that host a service. Service placement decisions are made in a way that accounts for performance and availability requirements of both the service being placed and other services.

A method for service creation and mapping between at least two support systems includes provisioning a service based on a service requirement and a plurality of available data centers, collecting a value of the service requirement and a cost associated with the service, matching resource types of the data centers to resource availability, determining at least one valid data center from the plurality of data centers, and selecting a valid data center with a minimum cost for service placement and provisioning.

An approach efficiently and dynamically places services within a multi-node system when expanding or contracting services, that is, increasing and decreasing the number of instances that host a service. Service placement decisions are made in a way that accounts for performance and availability requirements of both the service being placed and other services.

The invention discloses a dynamic service placement method based on Lyapunov control optimization in edge calculation. The mobile users can unload a large number of tasks to the adjacent edge servers for processing, so that the problem of shortage of user resources is relieved, and efficient mobile task execution is further realized by coordinating idle resources of the edge servers. Because the coverage range of the edge server is limited, in order to guarantee the service performance of the mobile user, the system needs to dynamically adjust the service placement of the user through service migration. According to the invention, long-term service migration budget is introduced; the problem that huge operation expenditure is generated due to excessive service migration of the user is avoided, and then the service performance and the operation expenditure of the mobile user are jointly optimized by utilizing Lyapunov queue transformation, so that the service performance of the user is effectively improved on the premise that the set migration budget is not exceeded.

Techniques to adaptively launch / replace applications and services on edge devices in a cellular infrastructure and / or adaptively place content and computation at the edge devices based on logic at the network core are provided. In one aspect, a method for dynamic placement of applications in a cellular network mobile cloud is provided which includes the steps of: (a) obtaining: (i) a model of the cellular network, (ii) a model of user mobility patterns in the cellular network, and (iii) a model of a profile of the applications; (b) upon receipt of requests from users for the applications, obtaining runtime states at edge servers in the mobile cloud; and (c) upon obtaining the runtime states at the edge servers, placing the requests among the edge servers and a core server in the mobile cloud based on the models obtained in step (a) and the runtime states obtained in step (b).

A method for service creation and mapping between at least two support systems includes provisioning a service based on a service requirement and a plurality of available data centers, collecting a value of the service requirement and a cost associated with the service, matching resource types of the data centers to resource availability, determining at least one valid data center from the plurality of data centers, and selecting a valid data center with a minimum cost for service placement and provisioning.

Several cloud observers monitor a set of clouds to collect cloud information and communicate the information to several service placement units thus forming a distributed service-placement system. The service-placement units communicate with a population of clients to receive service-assignment requests and select at least one compatible cloud for each request. The cloud observers share the cloud-monitoring workload and the service-placement units share the cloud-assignment workload. According to a first connectivity scheme, each cloud observer has a path to each service-placement unit. According to a second connectivity scheme, the cloud observers are interconnected to pool cloud information so that each cloud observer possesses cloud information of all clouds. Each cloud observer communicates with a respective subset of the service-placement units. According to a third connectivity scheme, cloud information is communicated through an intermediate stage of multicast distributors, each coupled to a respective subset of service-placement units.

A service partitioned into service components requiring different resources is assigned to at least one cloud of a set of clouds. With interdependent service components, a service-placement engine is configured to coordinate assignments of individual service components. The service-placement engine receives from a client a definition of each service component and indications of components interdependence. Each cloud that satisfies compliance requirements, capability requirements, and resource requirements of a service component is considered eligible to host the service component. Selection of a specific eligible cloud is based on an overall cloud merit which depends on the service-component definition and the location of the client.

Various embodiments relate to a method and apparatus for programmable and dynamic approach for end-to-end multi-domain L2 / L3 service placement in telecommunications networks including defining a DSM policy by defining each of a plurality of service realms, assigning a plurality of nodes to the plurality of service realms, assigning service-type flags to each of the plurality of nodes, assigning internetworking flags to at least one of a plurality of internetworking nodes and applying the DSM policy during L2 / L3 service creation. The DSM policy also can be defined to include all possible paths between any two endpoints in the context of a service.

A method implemented in a network for placing services at network devices is disclosed. The network contains a plurality of network devices and offers a set of services to subscribers. Subsets of the set of services are included in ordered service chains for subscribers. The method starts with determining a list of bandwidth consumption entities of a service for each service within the set of services, where each bandwidth consumption entity is based on one or more chains of services. Then a weight of each service is calculated based on the list of bandwidth consumption entities of the service and a service with a highest weight within the group of one or more services that have not been placed is selected. The selected service is then placed at a network device based at least partially on calculating bandwidth impacts to the network by the service.

A Utility for enabling an arbitrary number of mobile devices to be used to create a choreographed multimedia event exhibition while distributed over an arbitrary space. Each participating mobile device can present images, video, audio, and camera flash elements according to a pattern described by event attributes. As such each mobile device becomes a building block of the choregraphed multimedia event to form a specific exhibition. Event attributes can be distributed to participating mobile devices several ways including; messaging, local interfaces, position, location, motion, or programmatic defaults without the need for central orchestration or to communicate with one another or any external communication service during the exhibition. Participating mobile devices can join and leave an event exhibition at any time and will become synchronized with the event exhibition in progress. Example events are explained in detail and the usage of the event attributes is highlighted. The utility further enables the commercialization of individual choreographed multimedia event exhibitions. The commercialization is established by licensing terms dependent on the exhibition attributes such as placement, product and services placement, size of audience and rebroadcast capabilities.

The invention provides a task network access and service placement position selection method and a system, and the method comprises the following steps: obtaining a fitness function according to the obtained real-time state data of a current task and the parameter data of an edge calculation system; generating a plurality of random solutions of network access and service placement, wherein each random solution comprises a plurality of network access and service placement base stations of at least one task; obtaining a local optimal solution and a global optimal solution in each random solutionaccording to the fitness function; optimizing by using a particle swarm algorithm according to the obtained local optimal solution and global optimal solution to obtain optimal network access and service placement position coordinates, and obtaining a real network access and service placement position according to a preset transition probability; according to the method and the device, two attributes of delay and energy consumption can be considered at the same time, so that the time delay is lower, the energy consumption is lower, the QoS requirements of users are met, and the task unloadingrate is improved.

The invention relates to a mobile edge computing service placement strategy based on artificial intelligence, and the strategy comprises the following steps: carrying out the modeling of a whole network system, and needing to build a macro base station model, a small base station model, and a user model; dividing the delay model of the user into a switching delay model and a non-switching delay model, wherein the non-switching delay model is divided into a queuing delay model, a transmission delay model and a calculation delay model; establishing a queuing model of the user; after the steps are completed, calculating the total delay perceived by the user; and finally, modeling the problem by using a reinforcement learning method, and establishing a state space, an action space, a revenue signal and a Bellman equation, so that the problem can be solved through an algorithm based on artificial intelligence. The mobile edge computing service placement strategy based on artificial intelligence can provide base station selection and service placement decisions, and realizes network selection and service placement decoupling on the basis of controllable cost.

The invention provides an intelligent hybrid optimization method for service placement in mobile edge computing, which comprises the following steps of: converting a service placement problem in mobile edge computing into an optimization problem model through a particle swarm optimization model, and initializing the optimization problem model; iterating the model and adjusting the speed of the particles by utilizing a contraction factor; calculating the transmission delay, the calculation delay, the calculation energy consumption, the transmission energy consumption and the total service costof the system; calculating the corresponding total service cost of the system according to the speed and the position of the particle of each iteration, and updating the global optimal solution; further updating the globally optimal solution by adopting a simulated annealing algorithm; according to the method provided by the invention, the system performance of service placement can be remarkablyimproved, the total service cost of the system is reduced, and the system service delay is reduced.

The invention provides a dynamic extension and placement method and device for edge computing services, and the method comprises the steps: carrying out the dynamic extension and placement of the edge computing services according to the current respective work load intensity prediction result of each micro-service corresponding to each application in an edge computing platform and the current respective work performance evaluation result of each edge node; performing automatic expansion processing on the number of the micro-service copies according to the target data processing request so as to determine the scaling optimization number of the micro-service copies; and adopting a preset self-adaptive discrete binary particle swarm optimization algorithm to obtain a mapping relationship between each micro-service copy and each available edge node according to the scaling optimization number of the micro-service copies, the number of currently placeable edge nodes and the performance information, so as to respectively place each micro-service copy to the corresponding edge node. According to the invention, the automatic expansion reliability and effectiveness of the edge computing service can be improved under the conditions of unbalanced edge load and unreliable network state in the edge environment, and the accuracy and reliability of edge computing service placement are improved.

The embodiment of the invention provides a redundant service deployment method and device. The redundant service deployment method comprises the following steps: determining multiple to-be-deployed services contained in an application program request of a user, a resource demand of each to-be-deployed service and a communication delay demand among the to-be-deployed services, wherein each to-be-deployed service comprises a plurality of service copies; based on a preset service placement rule, allocating a physical machine node to each service copy of each service to be deployed, and screeningout a plurality of available placement decisions; calculating the reliability of the available placement decision according to the reliability of the physical machine node placed by the service copy of each to-be-deployed service and the reliability of the micro cloud; determining a final placement decision from the available placement decisions; and deploying the corresponding to-be-deployed service in the physical machine node of the micro-cloud based on the final placement decision. The resource requirement of each service of the application program, the inter-service communication requirement, the reliability of the physical machine node and the reliability factor of the micro cloud are comprehensively considered, and the high reliability of service deployment is ensured.

The invention discloses a service placement and bandwidth allocation method for edge environment-oriented video analysis application. The method comprises the following steps: B1) collecting edge environment system state information of a current time slot; B2) calculating a service placement decision and a bandwidth allocation decision by using a two-layer iterative algorithm based on the current edge environment system state information; B3) executing a service placement decision and a bandwidth allocation decision; and B4) calculating the service migration cost of each user according to a decision result, and calculating and updating the queue length to serve as one piece of edge environment system state information of the next time slot. Aiming at video analysis application in an edge computing system, the invention provides an online two-layer iterative service placement and bandwidth allocation method, the limited computing resources and bandwidth resources of edge nodes are efficiently used, and the service migration cost and the user experience are effectively balanced.

The invention firstly provides an edge computing model based on a near-earth orbit and a service placement method thereof, the edge computing model comprises a task unloading model for a user task set under multiple base stations, and the task unloading model comprises local execution or edge execution. Local execution resource limitation and edge execution resource limitation are created, the influence of the placement service of the near-earth orbit satellite is considered on the edge server on the unloading model, and a local cost model and an edge cost model are created by the limitations. Based on a cost model of the user tasks, a joint service placement and unloading decision problem with the goal of minimizing the total cost of all the user tasks is put forward, and a low-complexity service placement method is put forward. According to the edge calculation model based on the near-earth orbit, the timeliness problem of existing service placement is considered, the joint service placement and calculation unloading problem of the mobile edge calculation model is analyzed, and a low-complexity method of the joint problem is provided.

The invention discloses a service arrangement method for maximizing social welfare in an edge alliance, designs the horizontal cooperation of the edge alliance, and integrates all edge nodes of all EIPs. First, the present invention models the service allocation problem as a planning problem for maximizing social welfare. Then, the present invention proposes two EIP dynamic pricing methods to determine the bidding price for customers and the in-package price for other EIPs. Evaluation results based on two real datasets show that the proposed service placement model can increase the overall gain of EIP by up to 24.5%, while reducing the overall delay by 35.5%.

The invention discloses an online service placement method with data refreshing based on value space estimation. The method comprises the following steps: in a mobile edge computing scene, under the condition that service background data is refreshed, determining how to select a service to be placed by an edge server, and adopting the manner to minimize a communication load between the edge server and a cloud server; converting a service placement problem into a shortest path problem; estimating a node value in the shortest path problem, and obtaining a service placement strategy of a next time slice during operation; finally, serving the user in the edge server so as to reduce the communication load between the edge server and the cloud server.

The embodiment of the invention provides a redundant service deployment method and device. The redundant service deployment method comprises the following steps: determining multiple to-be-deployed services contained in an application program request of a user, a resource demand of each to-be-deployed service and a communication delay demand among the to-be-deployed services, wherein each to-be-deployed service comprises a plurality of service copies; based on a preset service placement rule, allocating a physical machine node to each service copy of each service to be deployed, and screeningout a plurality of available placement decisions; calculating the reliability of the available placement decision according to the reliability of the physical machine node placed by the service copy of each to-be-deployed service and the reliability of the micro cloud; determining a final placement decision from the available placement decisions; and deploying the corresponding to-be-deployed service in the physical machine node of the micro-cloud based on the final placement decision. The resource requirement of each service of the application program, the inter-service communication requirement, the reliability of the physical machine node and the reliability factor of the micro cloud are comprehensively considered, and the high reliability of service deployment is ensured.

The invention discloses a seamless service migration method based on user movement perception and a computer system, which are applied to a small cell edge computing system, and the method comprises the following steps: modeling a small cell edge computing system scene, and defining a multi-user service migration problem and an optimization target; cleaning data, and analyzing user movement characteristics; solving an optimal service placement decision according to user movement characteristics: dividing the users into two types according to association information between the users and the APs: if the users move to the preferred APs, considering service migration for the users, and adopting a migration strategy of reinforcement learning to make migration decisions for the users in real time; and if the user moves to the non-preferred AP, service migration does not need to be considered for the user, and the task corresponding to the service request of the user is still placed on the original edge node. According to the method, the influence of the service scheduling decision at the current moment on future system performance is considered, unnecessary migration in the system is reduced, the complexity of multi-user service migration is reduced, and a service migration optimization strategy with the aim of minimizing the long-term average delay of the system is provided.

The invention discloses a novel table tennis training table. The novel table tennis training table comprises a serving table body and a catching table body, wherein a net rack is arranged at the upperside of the joint of the serving table body and the catching table body, the heights of the upper surfaces of the serving table body and the catching table body are equal, the catching table body isformed by assembling 2-5 partition plates, a plurality of through ball entering holes are uniformly distributed in the upper surface of each partition plate, the positions of the ball entering holes in the partition plates are different, a ball collecting box is arranged below each partition plate, a closed ball outlet is arranged at the end part of each ball collecting box, a ball net with one end open is placed at the upper side of the catching table body, and the opening aligns at one side of the serving table body. The novel table tennis training table is novel in structure and convenientto disassemble, different combinations can be formed through splicing, the novel table tennis training table is used for assisting a trainee in carrying out service placement training and ball hittingprecision training, and the training effect is good.

The invention relates to the field of information technology, and discloses a virtual service migration method and system based on incremental minimum cost and maximum flow. The method includes: constructing a network topology map according to the geographical location and connection relationship of edge servers; The shortest path distance between servers, and generate the shortest path distance matrix; combined with the access information of each service, establish the minimum cost maximum flow model, the model includes service nodes and edge server nodes; solve the model to obtain the minimum cost maximum flow, migrate and place service nodes; update the access information of each service, and use the incremental minimum cost maximum flow algorithm to update the location of service nodes; carry out real-time monitoring and statistics, when the change rate of access information exceeds When setting the threshold, adjust its corresponding service node and edge server node. The invention can solve the optimal service placement and migration scheme in a short time, and is simple, reliable and easy to implement.

The invention discloses a service placement method for maximizing social welfare in an edge alliance, designs cooperation of the edge alliance in the horizontal direction, and integrates all edge nodes of all EIPs. Firstly, a service distribution problem is modeled into a planning problem for realizing social welfare maximization; then, the invention provides two EIP dynamic pricing methods so asto determine the bid for the client and the inner package price for other EIPs. Evaluation results based on two actual data sets show that the service placement model provided by the invention can enable the total gain of the EIP to be increased by 24.5% at most, and the total delay to be reduced by 35.5%.

The invention relates to a migration method for online VMEC service network selection, which belongs to the field of mobile communication. The method includes: S1: establishing a VMEC mathematical model; S2: scanning adjacent and accessible networks at time slot t, and performing network screening; S3: repeating S2 at time slot t+1 to obtain the initial time slot t+1 Network selection and migration placement as the initial strategy; S4: Network selection and service placement optimization: calculate and compare the three delays of the initial strategy for the t+1 time slot; update the time delay of the t+1 time slot based on the comparison results of the three delays The initial strategy obtains the optimized service migration strategy Φ(t+1) for time slot t+1; finally, the access network selection and service placement are performed according to the strategy Φ(t+1); S5: In the next time slot, repeat steps S3 and S4 , get access network selection and service migration strategy for each time slot in time T. The invention can minimize the time delay for the vehicle to select the network and formulate the service migration strategy during the moving process, and improve the service quality of the user.

The invention relates to the field of mobile edge computing, in particular to an edge computing service placement and request allocation method and system, which comprises the following steps that a cloud computing center collects information about service requests and computing resource states of user equipment and edge server nodes; the cloud computing center constructs an evolutionary game model of joint optimization request average time delay and edge server use cost according to the collected information; the cloud computing center executes an iterative algorithm of population game to solve the constructed game model, and obtains an evolution equilibrium strategy to serve as an optimal service placement and request distribution decision; The cloud computing center issues the optimal request distribution and service placement decision to each edge server node; and the edge server node executes optimal service placement and request allocation decision configuration and returns a configuration record to the cloud computing center. According to the method and device, service placement and user request allocation decisions are optimized, and the use cost of edge server nodes is reduced while the user experience quality and the service quality are ensured.

The invention discloses an online service placement method with data refresh based on value space estimation. In a mobile edge computing scenario, when there is service background data refresh, how to select a service for placement by an edge server is adopted to minimize the edge server The communication load with the cloud server; then the service placement problem is transformed into the shortest path problem; then the node value in the shortest path problem is estimated, and the service placement strategy for the next time slice is obtained at runtime; finally, it is achieved in the edge server Serve users and reduce the communication load between edge servers and cloud servers.